This project presents an AI model designed to predict outcomes in the Aviator game, focusing on forecasting multiplier values. The repository includes the model implementation, training scripts, data generation utilities, and a comprehensive set of visual evaluations to demonstrate its performance and trustworthiness. The goal is to provide an open-source, transparent, and well-documented solution for understanding and potentially predicting Aviator game dynamics.
Visual Assets
Aviator AI Model Trust Evaluation
Trust Score Card
Aviator Prediction Pipeline Architecture
How It Works Diagram
Model Performance: Actual Multipliers vs. Prediction Confidence
Performance Graph
Model Training Convergence
Training Loss Curve
Prediction Accuracy across Multiplier Thresholds
Accuracy Threshold Comparison
Model Architecture
The core of the prediction system is an LSTM (Long Short-Term Memory) neural network, a type of recurrent neural network well-suited for sequence prediction tasks. The model processes historical multiplier data to learn patterns and predict the probability of the next multiplier reaching a certain threshold.
Installation
To set up the project locally, follow these steps:
To generate synthetic data for training and evaluation:
python generate_aviator_data.py
This will create an aviator_data.csv file in the project directory.
Model Training
To train the Aviator AI model:
python train_aviator_model.py
This script will train the LSTM model and save the trained weights to aviator_model.pth.
Prediction
To use the trained model for predictions:
python
1import torch
2from aviator_predictor import AviatorLSTM, predict_next_multiplier
34# Load the model5model = AviatorLSTM()6model.load_state_dict(torch.load("aviator_model.pth"))78# Example sequence of past multipliers9sample_sequence =[1.2,2.5,1.1,3.4,1.5]1011# Make a prediction for the next round with a threshold of 1.5x12will_reach, confidence = predict_next_multiplier(model, sample_sequence, threshold=1.5)1314print(f"Prediction: {"High Probability" if will_reach else "Low Probability"} (Confidence: {confidence:.2f}%) ")
Performance Metrics and Evaluation
The visual assets provided in the assets/ folder offer a detailed overview of the model's performance, stability, and trust scores. Key metrics include:
Live Accuracy: Measures the model's accuracy on real-time data streams.
Data Integrity: Assesses the quality and consistency of the input data.
Model Stability: Evaluates the model's consistency in predictions over time.
Fairness Check: Ensures the model does not exhibit biases.
License
This project is licensed under the MIT License. See the LICENSE file for more details.
Citation
If you use this model or any part of this project in your research or work, please cite it as:
@misc{aviator_ai_predictor_2026,
author = {Manus AI},
title = {Aviator AI Predictor: An Open-Source AI Model for Aviator Game Forecasting},
year = {2026},
publisher = {Hugging Face},
journal = {Hugging Face Hub},
howpublished = {\url{https://huggingface.co/your-username/aviator-ai-predictor}}
}
Acknowledgments
Special thanks to the open-source community for providing invaluable tools and resources that made this project possible.
